|Plumbing the depths: extending ecological niche modelling and species distribution modelling in three dimensions|Bentlage, B.; Peterson, A.T.; Barve, N.; Cartwright, P. (2013). Plumbing the depths: extending ecological niche modelling and species distribution modelling in three dimensions. Glob. Ecol. Biogeogr. 22(8): 952-961. dx.doi.org/10.1111/geb.12049
In: Global Ecology and Biogeography. Blackwell Science: Oxford. ISSN 1466-822X, more
Periphylla periphylla (Péron & Lesueur, 1810) [WoRMS]
Bioclimatic envelope; depth; ecological niche model; marine; Periphyllaperiphylla; species distribution model; three-dimensional
|Authors|| || Top |
- Bentlage, B.
- Peterson, A.T.
- Barve, N.
- Cartwright, P.
Aim Ecological niche modelling (ENM) and species distribution modelling (SDM) have been used extensively to study biogeographic and macroecological patterns of terrestrial fauna and flora. Few studies to date have applied ENM and SDM to marine ecosystems, and those that have treated the marine environment as a two-dimensional space owing to limitations of the implementations of current ENM/SDM tools. For many marine organisms, ENM/SDM should be performed in three-dimensional space, taking into account latitude, longitude and depth. We present a case study demonstrating a strategy for three-dimensional ENM/SDM. Location Open ocean; global. Methods We decompose the three-dimensional structure of marine environmental and species occurrence data into a series of two-dimensional spaces using an easy-to-implement transformation, after which existing ENM/SDM tools can be used to analyse the data. We demonstrate our approach by modelling the potential distribution of a deep-sea-dwelling jellyfish with two commonly used algorithms. Potential effects of missing data and spatial sampling biases were assessed using resampling approaches. Results We demonstrate that it is feasible to derive predictive models of three-dimensional distributions of marine species using existing software tools developed with two-dimensional terrestrial situations in mind. The strategy presented here allowed us to model the distribution of a jellyfish species that inhabits the deep sea. We assessed the effects of missing occurrence data and spatial bias of occurrence data, and found that interpolation among occurrence data-points and extrapolation into unsampled conditions present distinct challenges that may require different modelling algorithms and interpretations. Main conclusions Our modified ENM/SDM approach is straightforward, and can be used to model situations that have heretofore been beyond the reach of ENM/SDM applications. In particular, geographic distributions and ecological niches of organisms inhabiting three-dimensional habitats such as water columns in marine and freshwater environments can be modelled using the framework presented here.